{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:54.918636Z",
     "start_time": "2023-10-07T11:55:54.915567Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:54.961385Z",
     "start_time": "2023-10-07T11:55:54.918265Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(397, 9)\n"
     ]
    },
    {
     "data": {
      "text/plain": "    mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n0  18.0          8         307.0        130    3504          12.0    70   \n1  15.0          8         350.0        165    3693          11.5    70   \n2  18.0          8         318.0        150    3436          11.0    70   \n3  16.0          8         304.0        150    3433          12.0    70   \n4  17.0          8         302.0        140    3449          10.5    70   \n\n   origin                       name  \n0       1  chevrolet chevelle malibu  \n1       1          buick skylark 320  \n2       1         plymouth satellite  \n3       1              amc rebel sst  \n4       1                ford torino  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mpg</th>\n      <th>cylinders</th>\n      <th>displacement</th>\n      <th>horsepower</th>\n      <th>weight</th>\n      <th>acceleration</th>\n      <th>year</th>\n      <th>origin</th>\n      <th>name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>18.0</td>\n      <td>8</td>\n      <td>307.0</td>\n      <td>130</td>\n      <td>3504</td>\n      <td>12.0</td>\n      <td>70</td>\n      <td>1</td>\n      <td>chevrolet chevelle malibu</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>15.0</td>\n      <td>8</td>\n      <td>350.0</td>\n      <td>165</td>\n      <td>3693</td>\n      <td>11.5</td>\n      <td>70</td>\n      <td>1</td>\n      <td>buick skylark 320</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.0</td>\n      <td>8</td>\n      <td>318.0</td>\n      <td>150</td>\n      <td>3436</td>\n      <td>11.0</td>\n      <td>70</td>\n      <td>1</td>\n      <td>plymouth satellite</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>16.0</td>\n      <td>8</td>\n      <td>304.0</td>\n      <td>150</td>\n      <td>3433</td>\n      <td>12.0</td>\n      <td>70</td>\n      <td>1</td>\n      <td>amc rebel sst</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17.0</td>\n      <td>8</td>\n      <td>302.0</td>\n      <td>140</td>\n      <td>3449</td>\n      <td>10.5</td>\n      <td>70</td>\n      <td>1</td>\n      <td>ford torino</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto = pd.read_csv(r'../data/Auto.csv')\n",
    "print(auto.shape)\n",
    "auto.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:55.614955Z",
     "start_time": "2023-10-07T11:55:54.929955Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:55.648576Z",
     "start_time": "2023-10-07T11:55:55.617419Z"
    }
   },
   "outputs": [],
   "source": [
    "auto['horsepower'] = auto['horsepower'].replace('?',np.nan)\n",
    "auto  = auto.dropna()\n",
    "auto['horsepower'] = auto['horsepower'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:55.648943Z",
     "start_time": "2023-10-07T11:55:55.620075Z"
    }
   },
   "outputs": [],
   "source": [
    "auto = auto.dropna()\n",
    "auto = auto.sort_values(by = ['horsepower'],ascending = True,axis = 0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:55.650557Z",
     "start_time": "2023-10-07T11:55:55.628915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n19   26.0          4          97.0          46    1835          20.5    70   \n102  26.0          4          97.0          46    1950          21.0    73   \n326  43.4          4          90.0          48    2335          23.7    80   \n325  44.3          4          90.0          48    2085          21.7    80   \n244  43.1          4          90.0          48    1985          21.5    78   \n\n     origin                             name  \n19        2     volkswagen 1131 deluxe sedan  \n102       2          volkswagen super beetle  \n326       2               vw dasher (diesel)  \n325       2             vw rabbit c (diesel)  \n244       2  volkswagen rabbit custom diesel  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mpg</th>\n      <th>cylinders</th>\n      <th>displacement</th>\n      <th>horsepower</th>\n      <th>weight</th>\n      <th>acceleration</th>\n      <th>year</th>\n      <th>origin</th>\n      <th>name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19</th>\n      <td>26.0</td>\n      <td>4</td>\n      <td>97.0</td>\n      <td>46</td>\n      <td>1835</td>\n      <td>20.5</td>\n      <td>70</td>\n      <td>2</td>\n      <td>volkswagen 1131 deluxe sedan</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>26.0</td>\n      <td>4</td>\n      <td>97.0</td>\n      <td>46</td>\n      <td>1950</td>\n      <td>21.0</td>\n      <td>73</td>\n      <td>2</td>\n      <td>volkswagen super beetle</td>\n    </tr>\n    <tr>\n      <th>326</th>\n      <td>43.4</td>\n      <td>4</td>\n      <td>90.0</td>\n      <td>48</td>\n      <td>2335</td>\n      <td>23.7</td>\n      <td>80</td>\n      <td>2</td>\n      <td>vw dasher (diesel)</td>\n    </tr>\n    <tr>\n      <th>325</th>\n      <td>44.3</td>\n      <td>4</td>\n      <td>90.0</td>\n      <td>48</td>\n      <td>2085</td>\n      <td>21.7</td>\n      <td>80</td>\n      <td>2</td>\n      <td>vw rabbit c (diesel)</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>43.1</td>\n      <td>4</td>\n      <td>90.0</td>\n      <td>48</td>\n      <td>1985</td>\n      <td>21.5</td>\n      <td>78</td>\n      <td>2</td>\n      <td>volkswagen rabbit custom diesel</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:55.663167Z",
     "start_time": "2023-10-07T11:55:55.636275Z"
    }
   },
   "outputs": [],
   "source": [
    "def fit_degree(data,var,target,degree):\n",
    "    poly = PolynomialFeatures(degree)\n",
    "    poly_data = poly.fit_transform(data[var].to_frame())\n",
    "    lin_model = LinearRegression(fit_intercept=False)\n",
    "    lin_model.fit(poly_data,data[target])\n",
    "    pred = lin_model.predict(poly_data)\n",
    "    \n",
    "    return pred\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:55.960716Z",
     "start_time": "2023-10-07T11:55:55.642761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x800 with 1 Axes>",
      "image/png": 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/Ctnoi2bFAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(auto,'horsepower','mpg',1)\n",
    "pred_2 = fit_degree(auto,'horsepower','mpg',2)\n",
    "pred_5 = fit_degree(auto,'horsepower','mpg',5)\n",
    "\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(auto['horsepower'],auto['mpg'])\n",
    "plt.plot(auto['horsepower'],pred_1,color = 'orange')\n",
    "plt.plot(auto['horsepower'],pred_2,color = 'green')\n",
    "plt.plot(auto['horsepower'],pred_5,color = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.762601Z",
     "start_time": "2023-10-07T11:55:55.950874Z"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Poly fit with degree 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.783600Z",
     "start_time": "2023-10-07T11:55:56.762441Z"
    }
   },
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(2)\n",
    "poly_data = poly.fit_transform(auto['horsepower'].to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.784398Z",
     "start_time": "2023-10-07T11:55:56.766275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.688\n",
      "Model:                            OLS   Adj. R-squared:                  0.686\n",
      "Method:                 Least Squares   F-statistic:                     428.0\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           5.40e-99\n",
      "Time:                        19:55:56   Log-Likelihood:                -1133.2\n",
      "No. Observations:                 392   AIC:                             2272.\n",
      "Df Residuals:                     389   BIC:                             2284.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         56.9001      1.800     31.604      0.000      53.360      60.440\n",
      "x1            -0.4662      0.031    -14.978      0.000      -0.527      -0.405\n",
      "x2             0.0012      0.000     10.080      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                       16.158   Durbin-Watson:                   1.663\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               30.662\n",
      "Skew:                           0.218   Prob(JB):                     2.20e-07\n",
      "Kurtosis:                       4.299   Cond. No.                     1.29e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.29e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "est = sm.OLS(auto['mpg'], poly_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# linear fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.784560Z",
     "start_time": "2023-10-07T11:55:56.774799Z"
    }
   },
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "poly_data = poly.fit_transform(auto['horsepower'].to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.829862Z",
     "start_time": "2023-10-07T11:55:56.780989Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.606\n",
      "Model:                            OLS   Adj. R-squared:                  0.605\n",
      "Method:                 Least Squares   F-statistic:                     599.7\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           7.03e-81\n",
      "Time:                        19:55:56   Log-Likelihood:                -1178.7\n",
      "No. Observations:                 392   AIC:                             2361.\n",
      "Df Residuals:                     390   BIC:                             2369.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         39.9359      0.717     55.660      0.000      38.525      41.347\n",
      "x1            -0.1578      0.006    -24.489      0.000      -0.171      -0.145\n",
      "==============================================================================\n",
      "Omnibus:                       16.432   Durbin-Watson:                   1.319\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.305\n",
      "Skew:                           0.492   Prob(JB):                     0.000175\n",
      "Kurtosis:                       3.299   Cond. No.                         322.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "est = sm.OLS(auto['mpg'], poly_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# poly fit with degree 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.830260Z",
     "start_time": "2023-10-07T11:55:56.788454Z"
    }
   },
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(2)\n",
    "poly_data = poly.fit_transform(auto['horsepower'].to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.831119Z",
     "start_time": "2023-10-07T11:55:56.793214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.688\n",
      "Model:                            OLS   Adj. R-squared:                  0.686\n",
      "Method:                 Least Squares   F-statistic:                     428.0\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           5.40e-99\n",
      "Time:                        19:55:56   Log-Likelihood:                -1133.2\n",
      "No. Observations:                 392   AIC:                             2272.\n",
      "Df Residuals:                     389   BIC:                             2284.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         56.9001      1.800     31.604      0.000      53.360      60.440\n",
      "x1            -0.4662      0.031    -14.978      0.000      -0.527      -0.405\n",
      "x2             0.0012      0.000     10.080      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                       16.158   Durbin-Watson:                   1.663\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               30.662\n",
      "Skew:                           0.218   Prob(JB):                     2.20e-07\n",
      "Kurtosis:                       4.299   Cond. No.                     1.29e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.29e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "est = sm.OLS(auto['mpg'], poly_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# residual plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For degree 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:56.941087Z",
     "start_time": "2023-10-07T11:55:56.805461Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x12f86b310>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_1 = auto['mpg'] - pred_1\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.scatterplot(x = auto['mpg'],y = error_1)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',color = 'black',linewidth = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for degree 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:57.069884Z",
     "start_time": "2023-10-07T11:55:56.931345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x12f9044f0>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_2 = auto['mpg'] - pred_2\n",
    "\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.scatterplot(x = auto['mpg'],y = error_2)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',color = 'black',linewidth = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:57.070141Z",
     "start_time": "2023-10-07T11:55:57.063093Z"
    }
   },
   "outputs": [],
   "source": [
    "# we can see the that the former graph has kind of U pattern, and thus it does not follow a linear relationship,\n",
    "# on the other hand the latter graph has no such kind of definate pattern, and is dispersed quite well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T11:55:57.084290Z",
     "start_time": "2023-10-07T11:55:57.069400Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.15 ('py309')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "vscode": {
   "interpreter": {
    "hash": "6f6b82f41707301100400135f07b8b857f2b71e68564d2b5d8f08e30d7b274ea"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
